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Record W4401613482 · doi:10.1037/ocp0000384

Supportive-leadership training to improve social connection: A cluster-randomized trial demonstrating efficacy in a high-risk occupational context.

2024· article· en· W4401613482 on OpenAlexaff
Cynthia D. Mohr, Leslie B. Hammer, Jennifer K. Dimoff, Shalene Allen, James D. Lee, Sarah Arpin, Sheila McCabe, Krista Brockwood, Todd Bodner, L. Meghan Mahoney, Michael N. Dretsch, Thomas W. Britt

Bibliographic record

VenueJournal of Occupational Health Psychology · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicYouth Development and Social Support
Canadian institutionsUniversity of Ottawa
FundersCenters for Disease Control and PreventionU.S. Department of Health and Human ServicesNational Institute for Occupational Safety and HealthPortland State UniversityU.S. Department of Defense
KeywordsPsychologyContext (archaeology)Applied psychologyCluster (spacecraft)Connection (principal bundle)Social psychologyRandomized controlled trialOccupational stressMedicineComputer science

Abstract

fetched live from OpenAlex

= 158). Additionally, service members with higher baseline loneliness were more strongly and positively impacted by the supervisor training, reporting higher levels of supportive behaviors from their leaders at 3 months postbaseline. In sum, these results suggest how workplaces, especially those that are considered high-risk occupations, and their leaders play a critical role in a national strategy to address Americans' well-being. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0090.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.199
GPT teacher head0.480
Teacher spread0.281 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designRandomized trial
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations21
Published2024
Admission routes1
Has abstractyes

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